Stability of Analytic Neural Networks With Event-Triggered Synaptic Feedbacks

作者：Zheng, Ren^*; Yi, Xinlei^*; Lu, Wenlian^*; Chen, Tianping^*

来源：IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2): 483-494.

DOI：10.1109/TNNLS.2015.2488903

• Almost stability
• analytic neural network
• event-triggered rule
• Zenoa behaviors

In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules. This model is general and include Hopfield neural network as a special case. These event-trigger rules can efficiently reduces loads of computation and information transmission at synapses of the neurons. The synaptic feedback of each neuron keeps a constant value based on the outputs of the other neurons at its latest triggering time but changes at its next triggering time, which is determined by a certain criterion. It is proved that every trajectory of the analytic neural network converges to certain equilibrium under this event-triggered rule for all the initial values except a set of zero measure. The main technique of the proof is the Lojasiewicz inequality to prove the finiteness of trajectory length. The realization of this event-triggered rule is verified by the exclusion of Zeno behaviors. Numerical examples are provided to illustrate the efficiency of the theoretical results.

• 出版日期2016-2
• 单位复旦大学